LLM Inference

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Scoured 98 posts in 8.3 ms

Nvidia DGX Spark GB10 – AI Models and Guide with vLLM and Autonomous Script

 🧠LLM  Content type: Code
github.com··Hacker News

RKSC: Reasoning-Aware KV Cache Sharing and Confident Early Exit for Multi-Step LLM Inference

 🧠LLM  Content type: Academic
arxiv.org·

LLM Inference Handbook 2026

 🧠LLM
pub.towardsai.net
·

Inferoa AI harness claimed 90% cache savings. We ran it and measured 97.8%

 📡Observability
zozo123.github.io··Hacker News

A system programmer’s guide to LLM inference

 🧠LLM  Content type: Blog

GGUF vs GPTQ vs AWQ: The Plain-English Guide to LLM Quantization (and Which One to Pick)

 💬NLP
Less-relevant results

DiffusionGemma: The Developer Guide- Google Developers Blog

 📡Information Theory  Content type: Blog

google/gemma-4-31B-it · fix: chat template — null handling, reasoning preservation, turn-tag balance, input validation

 🧠Context Engineering

Youssof Altoukhi (@Youssofal_)

 ♟️Game Theory
xcancel.com··r/LocalLLaMA

Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation

 🧠LLM  Content type: Academic
arxiv.org·

harshuljain13/llm-inference-at-scale: A Practitioner handbook for production llm serving.

 💬LLMs  Content type: Code
github.com··Hacker News

Optimizing Local LLM Inference on Constrained Hardware

 🧠LLM
pub.towardsai.net
·

STAR-KV: Low-Rank KV Cache Compression via Soft Thresholding for Adaptive Rank Control

 🧠LLM  Content type: Academic
arxiv.org·

heterodoxin/graphkv: Graph-guided KV cache compression for memory-efficient LLM inference.

 🧠LLM  Content type: Code
github.com··r/LocalLLaMA

Breaking the Ice: Analyzing Cold Start Latency in vLLM

 Side-Channel Attacks  Content type: Academic
arxiv.org··Hacker News

How LLM Quantization Works: INT8, INT4, GPTQ, and AWQ Explained

 🧠LLM
pub.towardsai.net
·

SpectrumKV: Per-Token Mixed-Precision KV Cache Transfer for Prefill-Decode Disaggregated LLM Serving

 🧠LLM  Content type: Academic
arxiv.org·

huawei-csl/KVarN: KVarN is a native vLLM KV-cache quantization backend for your agents: 3-5x more context, throughput above FP16, and FP16-level accuracy. Calibration-free, one flag.

 🎯AI Agents  Content type: Code
github.com··Hacker News

ReasonAlloc: Hierarchical Decoding-Time KV Cache Budget Allocation for Reasoning Models

 💬NLP  Content type: Academic
arxiv.org·

Clairvoyant: Predictive SJF Scheduling to Mitigate Head-of-Line Blocking in Serial LLM Backends

 🧠LLM  Content type: Academic
arxiv.org·

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